This markdown represents a summary of the functional connectivity (FC) results, i.e. how changes in resting state functional connectivity (RSFC) relate to memory performance. This captures post-encoding/consolition processes. Addionally, the concatenated time course of the task data was used to investigate functional connectivity during online and offline encoding and how this relates to memory performance.
To calculate the total # of items encoded, for each subject, a sum score was created adding up all items remembered
# one sample t-test to see whether recognition performance was above chance
describe(dfWide$allConf_abs)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 50 22.54 4.12 22 22.43 4.45 15 32 17 0.2 -0.6 0.58
t.test(dfWide$allConf_abs, mu = (length(unique(dfLong$stimID))/4), alternative = "greater")
##
## One Sample t-test
##
## data: dfWide$allConf_abs
## t = 23.257, df = 49, p-value < 2.2e-16
## alternative hypothesis: true mean is greater than 9
## 95 percent confidence interval:
## 21.56393 Inf
## sample estimates:
## mean of x
## 22.54
effsize::cohen.d(dfWide$allConf_abs, f = NA, mu = (length(unique(dfLong$stimID))/4))
##
## Cohen's d (single sample)
##
## d estimate: 3.28903 (large)
## Reference mu: 9
## 95 percent confidence interval:
## lower upper
## 2.417288 4.160771
# two sample t-test to see whether high confidence recognition differed between both groups
describe(dfWide$highConf_abs)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 50 15.14 5.35 15 15.15 4.45 3 27 24 -0.07 -0.13 0.76
describe.by(dfWide$highConf_abs, dfWide$group)
## Warning: describe.by is deprecated. Please use the describeBy function
##
## Descriptive statistics by group
## group: cont
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 15.52 4.65 15 15.57 4.45 3 25 22 -0.23 0.6 0.93
## ------------------------------------------------------------
## group: exp
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 14.76 6.04 16 14.57 5.93 4 27 23 0.08 -0.72 1.21
t.test(dfWide$highConf_abs ~ dfWide$group)
##
## Welch Two Sample t-test
##
## data: dfWide$highConf_abs by dfWide$group
## t = 0.49846, df = 45.028, p-value = 0.6206
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.310821 3.830821
## sample estimates:
## mean in group cont mean in group exp
## 15.52 14.76
effsize::cohen.d(dfWide$allConf_abs, f = dfWide$group)
##
## Cohen's d
##
## d estimate: 0.4230503 (small)
## 95 percent confidence interval:
## lower upper
## -0.1519691 0.9980698
To follow suggestions by others investigating post-encoding rest (e.g., Duncan et al., 2014, Tompary et al., 2015), a corrected high confidence memory score was calculated to account for correct guesses using the formula below:
Corrected memory score = sum(high condidence correct) - sum ((high confidence incorrect) / number of alternatives)
# two sample t-test to see whether corrected high confidence recognition differed between both groups
describe(dfWide$memory_corrected)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 50 13.86 5.42 13.88 13.81 4.45 2.75 26.25 23.5 0.05 -0.33 0.77
describe.by(dfWide$memory_corrected, dfWide$group)
## Warning: describe.by is deprecated. Please use the describeBy function
##
## Descriptive statistics by group
## group: cont
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 14.26 4.72 14 14.14 4.45 2.75 24 21.25 0.05 0.1 0.94
## ------------------------------------------------------------
## group: exp
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 13.45 6.11 13.75 13.2 7.41 3.75 26.25 22.5 0.13 -0.82 1.22
t.test(dfWide$memory_corrected ~ dfWide$group)
##
## Welch Two Sample t-test
##
## data: dfWide$memory_corrected by dfWide$group
## t = 0.52492, df = 45.122, p-value = 0.6022
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.297721 3.917721
## sample estimates:
## mean in group cont mean in group exp
## 14.26 13.45
effsize::cohen.d(dfWide$memory_corrected, f = dfWide$group)
##
## Cohen's d
##
## d estimate: 0.1484696 (negligible)
## 95 percent confidence interval:
## lower upper
## -0.4210068 0.7179459
For each subject, the correlation between mean-centred curiosity ratings and high confidence recogntion is computed and correlation coefficients are Fisher’s z-transformed.
# two sample t-test to see whether within-person correlation (z transformed) differed between both groups
describe(dfWide$cor_cur_mem_z)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 50 0.03 0.19 0.05 0.05 0.19 -0.45 0.34 0.79 -0.56 -0.1 0.03
describe.by(dfWide$cor_cur_mem_z, dfWide$group)
## Warning: describe.by is deprecated. Please use the describeBy function
##
## Descriptive statistics by group
## group: cont
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 0.06 0.21 0.06 0.08 0.2 -0.45 0.34 0.79 -0.81 -0.04 0.04
## ------------------------------------------------------------
## group: exp
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 0.01 0.17 -0.01 0.02 0.17 -0.4 0.33 0.73 -0.25 -0.3 0.03
t.test(dfWide$cor_cur_mem_z ~ dfWide$group)
##
## Welch Two Sample t-test
##
## data: dfWide$cor_cur_mem_z by dfWide$group
## t = 0.84204, df = 46.073, p-value = 0.4041
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.06250556 0.15241609
## sample estimates:
## mean in group cont mean in group exp
## 0.05632509 0.01136982
effsize::cohen.d(dfWide$cor_cur_mem_z, f = dfWide$group)
##
## Cohen's d
##
## d estimate: 0.2381651 (small)
## 95 percent confidence interval:
## lower upper
## -0.3325409 0.8088710
Previous research (Gruber et al., 2014) has quantified the effects of curiosity on memory using an index referred to as curiosity-motivated learning index, comparing the number of items later remembered vs. forgotten in states of high vs. low curiosity.
\[ CDMB = {\sum_{i=1}^{36} curiosity_{CWC} > 0 * memory_{Dummy} \over \sum_{i=1}^{36} curiosity_{CWC} > 0 \ } - {\sum_{i=1}^{36} curiosity_{CWC} < 0 * memory_{Dummy} \over \sum_{i=1}^{36} curiosity_{CWC} < 0 \ } \]
As such, the CDMB computes the ratio between items remembered eliciting high ratings of curiosity to the total number of items eliciting high curiosity ratings and campares it to the ratio of items remembered eliciting low ratings of curiosity compared to the total number of items receiving low curiosity ratings.
To determine high vs low ratings of curiosity, curiosity was centred within cluster (CWC) and compared against 0.
When computing the product between curiosity and memory encoding (dummy-coded), mean-centred curiosity ratings can be used as a continuous variable or as a binary variable. In either way, the resulting CDMB are highly correlated with each other (r < .90). To ease the understanding, a binary formulation of mean-centred curiosity is used, allowing for the following definition of CDMB:
“To compute the effect of curiosity on encoding, the curiosity-driven memory benefit index was computed, i.e., the difference in high confidence recognition memory performance between magic tricks eliciting high ratings of curiosity relative to the total number of magic tricks eliciting high ratings of curiosity and the high confidence recognition memory performance between magic tricks eliciting low ratings of curiosity relative to the total number of magic tricks eliciting low ratings of curiosity.”
# two sample t-test to see whether CDMB (dichotomous) differed between both groups
describe(dfWide$CDMB)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 50 0.02 0.22 0.04 0.04 0.17 -0.86 0.36 1.22 -1.31 3.2 0.03
describe.by(dfWide$CDMB, dfWide$group)
## Warning: describe.by is deprecated. Please use the describeBy function
##
## Descriptive statistics by group
## group: cont
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 0.08 0.21 0.1 0.1 0.22 -0.43 0.36 0.79 -0.74 -0.26 0.04
## ------------------------------------------------------------
## group: exp
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 25 -0.05 0.22 -0.01 -0.02 0.17 -0.86 0.2 1.06 -2.02 5.2 0.04
t.test(dfWide$CDMB ~ dfWide$group)
##
## Welch Two Sample t-test
##
## data: dfWide$CDMB by dfWide$group
## t = 2.139, df = 47.908, p-value = 0.03756
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.007703609 0.249235574
## sample estimates:
## mean in group cont mean in group exp
## 0.08333497 -0.04513463
effsize::cohen.d(dfWide$CDMB, f = dfWide$group)
##
## Cohen's d
##
## d estimate: 0.6050002 (medium)
## 95 percent confidence interval:
## lower upper
## 0.0234426 1.1865579
# re-run t test after rmoving outlier
q25 <- as.numeric(quantile(dfWide$CDMB)[2])
q75 <- as.numeric(quantile(dfWide$CDMB)[4])
iqr <- q75 - q25# calculate IQR
tmp <- subset(dfWide, CDMB >= q25 - 1.5*iqr & CDMB <= q75 + 1.5*iqr) # this removes sub-control049 and sub-experimental018
t.test(tmp$CDMB ~ tmp$group)
##
## Welch Two Sample t-test
##
## data: tmp$CDMB by tmp$group
## t = 2.4882, df = 43.029, p-value = 0.01679
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.02199897 0.21017062
## sample estimates:
## mean in group cont mean in group exp
## 0.10478384 -0.01130095
effsize::cohen.d(tmp$CDMB, f = tmp$group)
##
## Cohen's d
##
## d estimate: 0.7182767 (medium)
## 95 percent confidence interval:
## lower upper
## 0.1187597 1.3177936
Below, all behavioural indices described above are combined in a scatter plot matrix.
While measures quantifying the same construct are highly correlated, measures of different constructs are uncorrelated.
Pearson's product-moment correlation
data: dfWide\(highConf_abs and dfWide\)memory_corrected t = 40.492, df = 48, p-value < 2.2e-16 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.9747652 0.9918884 sample estimates: cor 0.9856758
Pearson's product-moment correlation
data: dfWide\(CDMB and dfWide\)cor_cur_mem_z t = 11.3, df = 48, p-value = 3.987e-15 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.7528223 0.9139862 sample estimates: cor 0.8525262
# pre learning
mean(dfWide$FC_pre_s4)
## [1] 0.0347034
sd(dfWide$FC_pre_s4)
## [1] 0.02516238
# post learning
mean(dfWide$FC_post_s4)
## [1] 0.03878373
sd(dfWide$FC_post_s4)
## [1] 0.03042794
# diff
mean(dfWide$FC_diff_s4)
## [1] 0.004080335
sd(dfWide$FC_diff_s4)
## [1] 0.030998
# RSFC change across whole sample
t.test(dfWide[, "FC_diff_s4"], alternative = "greater")
##
## One Sample t-test
##
## data: dfWide[, "FC_diff_s4"]
## t = 0.93078, df = 49, p-value = 0.1783
## alternative hypothesis: true mean is greater than 0
## 95 percent confidence interval:
## -0.003269295 Inf
## sample estimates:
## mean of x
## 0.004080335
effsize::cohen.d(dfWide$FC_diff_s4, f = NA)
##
## Cohen's d (single sample)
##
## d estimate: 0.1316322 (negligible)
## Reference mu: 0
## 95 percent confidence interval:
## lower upper
## -0.4373767 0.7006411
# RSFC between both groups
t.test(dfWide[, "FC_diff_s4"] ~ dfWide$group)
##
## Welch Two Sample t-test
##
## data: dfWide[, "FC_diff_s4"] by dfWide$group
## t = 0.78748, df = 47.816, p-value = 0.4349
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01076769 0.02463001
## sample estimates:
## mean in group cont mean in group exp
## 0.0075459160 0.0006147536
sd(dfWide$FC_diff_s4[dfWide$group == "cont"])
## [1] 0.03206868
sd(dfWide$FC_diff_s4[dfWide$group == "exp"])
## [1] 0.03013914
effsize::cohen.d(dfWide$FC_diff_s4, f = dfWide$group)
##
## Cohen's d
##
## d estimate: 0.2227318 (small)
## 95 percent confidence interval:
## lower upper
## -0.3477221 0.7931858
cor <- "pearson"
# whole sample
if (cor == "spearman") {
DescTools::SpearmanRho(dfWide[, "highConf_abs"], dfWide[, "FC_diff_s4"], conf.level = 0.95)
} else {
cor.test(dfWide[, "highConf_abs"], dfWide[, "FC_diff_s4"])
}
##
## Pearson's product-moment correlation
##
## data: dfWide[, "highConf_abs"] and dfWide[, "FC_diff_s4"]
## t = 0.55646, df = 48, p-value = 0.5805
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2028063 0.3505956
## sample estimates:
## cor
## 0.08006087
# control
if (cor == "spearman") {
cor_cont <- DescTools::SpearmanRho(dfWide[dfWide$group == "cont", "highConf_abs"], dfWide[dfWide$group == "cont", paste0("FC_diff_s4")], conf.level = 0.95)
} else {
cor_cont <- cor.test(dfWide[dfWide$group == "cont", "highConf_abs"], dfWide[dfWide$group == "cont", paste0("FC_diff_s4")])
}
cor_cont
##
## Pearson's product-moment correlation
##
## data: dfWide[dfWide$group == "cont", "highConf_abs"] and dfWide[dfWide$group == "cont", paste0("FC_diff_s4")]
## t = -1.5778, df = 23, p-value = 0.1283
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6298669 0.0942553
## sample estimates:
## cor
## -0.3125146
# experimental
if (cor == "spearman") {
cor_exp <- DescTools::SpearmanRho(dfWide[dfWide$group == "exp", "highConf_abs"], dfWide[dfWide$group == "exp", paste0("FC_diff_s4")], conf.level = 0.95)
} else {
cor_exp <- cor.test(dfWide[dfWide$group == "exp", "highConf_abs"], dfWide[dfWide$group == "exp", paste0("FC_diff_s4")])
}
cor_exp
##
## Pearson's product-moment correlation
##
## data: dfWide[dfWide$group == "exp", "highConf_abs"] and dfWide[dfWide$group == "exp", paste0("FC_diff_s4")]
## t = 2.0276, df = 23, p-value = 0.05434
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.006754121 0.679926444
## sample estimates:
## cor
## 0.389416
# group effect
if (cor == "spearman") {
cocor::cocor.indep.groups(cor_exp[1], cor_cont[1], 25, 25, alternative = "two.sided",
test = "all", alpha = 0.05, conf.level = 0.95, null.value = 0,
data.name = NULL, var.labels = NULL, return.htest = FALSE)
} else {
cocor::cocor.indep.groups(cor_exp$estimate, cor_cont$estimate, 25, 25, alternative = "two.sided",
test = "all", alpha = 0.05, conf.level = 0.95, null.value = 0,
data.name = NULL, var.labels = NULL, return.htest = FALSE)
}
##
## Results of a comparison of two correlations based on independent groups
##
## Comparison between r1.jk = 0.3894 and r2.hm = -0.3125
## Difference: r1.jk - r2.hm = 0.7019
## Group sizes: n1 = 25, n2 = 25
## Null hypothesis: r1.jk is equal to r2.hm
## Alternative hypothesis: r1.jk is not equal to r2.hm (two-sided)
## Alpha: 0.05
##
## fisher1925: Fisher's z (1925)
## z = 2.4359, p-value = 0.0149
## Null hypothesis rejected
##
## zou2007: Zou's (2007) confidence interval
## 95% confidence interval for r1.jk - r2.hm: 0.1341 1.1322
## Null hypothesis rejected (Interval does not include 0)
cor <- "pearson"
# whole sample
if (cor == "spearman") {
DescTools::SpearmanRho(dfWide[, "CDMB"], dfWide[, "FC_diff_s4"], conf.level = 0.95)
} else {
cor.test(dfWide[, "CDMB"], dfWide[, "FC_diff_s4"])
}
##
## Pearson's product-moment correlation
##
## data: dfWide[, "CDMB"] and dfWide[, "FC_diff_s4"]
## t = 0.51151, df = 48, p-value = 0.6113
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2090009 0.3449091
## sample estimates:
## cor
## 0.07363021
# control
if (cor == "spearman") {
cor_cont <- DescTools::SpearmanRho(dfWide[dfWide$group == "cont", "CDMB"], dfWide[dfWide$group == "cont", paste0("FC_diff_s4")], conf.level = 0.95)
} else {
cor_cont <- cor.test(dfWide[dfWide$group == "cont", "CDMB"], dfWide[dfWide$group == "cont", paste0("FC_diff_s4")])
}
cor_cont
##
## Pearson's product-moment correlation
##
## data: dfWide[dfWide$group == "cont", "CDMB"] and dfWide[dfWide$group == "cont", paste0("FC_diff_s4")]
## t = -0.87836, df = 23, p-value = 0.3888
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5370550 0.2314527
## sample estimates:
## cor
## -0.180154
# experimental
if (cor == "spearman") {
cor_exp <- DescTools::SpearmanRho(dfWide[dfWide$group == "exp", "CDMB"], dfWide[dfWide$group == "exp", paste0("FC_diff_s4")], conf.level = 0.95)
} else {
cor_exp <- cor.test(dfWide[dfWide$group == "exp", "CDMB"], dfWide[dfWide$group == "exp", paste0("FC_diff_s4")])
}
cor_exp
##
## Pearson's product-moment correlation
##
## data: dfWide[dfWide$group == "exp", "CDMB"] and dfWide[dfWide$group == "exp", paste0("FC_diff_s4")]
## t = 1.3409, df = 23, p-value = 0.193
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1408479 0.6005078
## sample estimates:
## cor
## 0.2692687
# group effect
if (cor == "spearman") {
cocor::cocor.indep.groups(cor_exp[1], cor_cont[1], 25, 25, alternative = "two.sided",
test = "all", alpha = 0.05, conf.level = 0.95, null.value = 0,
data.name = NULL, var.labels = NULL, return.htest = FALSE)
} else {
cocor::cocor.indep.groups(cor_exp$estimate, cor_cont$estimate, 25, 25, alternative = "two.sided",
test = "all", alpha = 0.05, conf.level = 0.95, null.value = 0,
data.name = NULL, var.labels = NULL, return.htest = FALSE)
r_cmle <- cocor::cocor.indep.groups(cor_exp$estimate, cor_cont$estimate, 25, 25, alternative = "two.sided",
test = "all", alpha = 0.05, conf.level = 0.95, null.value = 0,
data.name = NULL, var.labels = NULL, return.htest = FALSE)
}
# whole sample
cor.test(dfWide[, "memory_corrected"], dfWide[, "FC_diff_s4"])
##
## Pearson's product-moment correlation
##
## data: dfWide[, "memory_corrected"] and dfWide[, "FC_diff_s4"]
## t = 0.45423, df = 48, p-value = 0.6517
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2168751 0.3376215
## sample estimates:
## cor
## 0.06542191
# control
cor_cont <- cor.test(dfWide[dfWide$group == "cont", "memory_corrected"], dfWide[dfWide$group == "cont", paste0("FC_diff_s4")])
# experimental
cor_exp <- cor.test(dfWide[dfWide$group == "exp", "memory_corrected"], dfWide[dfWide$group == "exp", paste0("FC_diff_s4")])
# group effect
cocor::cocor.indep.groups(cor_exp$estimate, cor_cont$estimate, 25, 25, alternative = "two.sided",
test = "all", alpha = 0.05, conf.level = 0.95, null.value = 0,
data.name = NULL, var.labels = NULL, return.htest = FALSE)
##
## Results of a comparison of two correlations based on independent groups
##
## Comparison between r1.jk = 0.3199 and r2.hm = -0.2603
## Difference: r1.jk - r2.hm = 0.5802
## Group sizes: n1 = 25, n2 = 25
## Null hypothesis: r1.jk is equal to r2.hm
## Alternative hypothesis: r1.jk is not equal to r2.hm (two-sided)
## Alpha: 0.05
##
## fisher1925: Fisher's z (1925)
## z = 1.9834, p-value = 0.0473
## Null hypothesis rejected
##
## zou2007: Zou's (2007) confidence interval
## 95% confidence interval for r1.jk - r2.hm: 0.0028 1.0393
## Null hypothesis rejected (Interval does not include 0)
# whole sample
cor.test(dfWide[, "cor_cur_mem_z"], dfWide[, "FC_diff_s4"])
##
## Pearson's product-moment correlation
##
## data: dfWide[, "cor_cur_mem_z"] and dfWide[, "FC_diff_s4"]
## t = -0.64375, df = 48, p-value = 0.5228
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3615560 0.1907405
## sample estimates:
## cor
## -0.09251931
# control
cor_cont <- cor.test(dfWide[dfWide$group == "cont", "cor_cur_mem_z"], dfWide[dfWide$group == "cont", paste0("FC_diff_s4")])
cor_cont
##
## Pearson's product-moment correlation
##
## data: dfWide[dfWide$group == "cont", "cor_cur_mem_z"] and dfWide[dfWide$group == "cont", paste0("FC_diff_s4")]
## t = -1.4352, df = 23, p-value = 0.1647
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6124445 0.1222925
## sample estimates:
## cor
## -0.2866918
# experimental
cor_exp <- cor.test(dfWide[dfWide$group == "exp", "cor_cur_mem_z"], dfWide[dfWide$group == "exp", paste0("FC_diff_s4")])
cor_exp
##
## Pearson's product-moment correlation
##
## data: dfWide[dfWide$group == "exp", "cor_cur_mem_z"] and dfWide[dfWide$group == "exp", paste0("FC_diff_s4")]
## t = 0.61013, df = 23, p-value = 0.5478
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2830414 0.4965722
## sample estimates:
## cor
## 0.1262039
# group effect
cocor::cocor.indep.groups(cor_exp$estimate, cor_cont$estimate, 25, 25, alternative = "two.sided",
test = "all", alpha = 0.05, conf.level = 0.95, null.value = 0,
data.name = NULL, var.labels = NULL, return.htest = FALSE)
##
## Results of a comparison of two correlations based on independent groups
##
## Comparison between r1.jk = 0.1262 and r2.hm = -0.2867
## Difference: r1.jk - r2.hm = 0.4129
## Group sizes: n1 = 25, n2 = 25
## Null hypothesis: r1.jk is equal to r2.hm
## Alternative hypothesis: r1.jk is not equal to r2.hm (two-sided)
## Alpha: 0.05
##
## fisher1925: Fisher's z (1925)
## z = 1.3991, p-value = 0.1618
## Null hypothesis retained
##
## zou2007: Zou's (2007) confidence interval
## 95% confidence interval for r1.jk - r2.hm: -0.1657 0.9061
## Null hypothesis retained (Interval includes 0)
cor <- "spearman"
# whole sample
if (cor == "spearman") {
DescTools::SpearmanRho(dfWide[, "highConf_abs"], dfWide[, "FC_diff_s4"], conf.level = 0.95)
} else {
cor.test(dfWide[, "highConf_abs"], dfWide[, "FC_diff_s4"])
}
## Registered S3 method overwritten by 'DescTools':
## method from
## print.palette wesanderson
## rho lwr.ci upr.ci
## 0.1495985 -0.1343433 0.4108387
# control
if (cor == "spearman") {
cor_cont <- DescTools::SpearmanRho(dfWide[dfWide$group == "cont", "highConf_abs"], dfWide[dfWide$group == "cont", paste0("FC_diff_s4")], conf.level = 0.95)
} else {
cor_cont <- cor.test(dfWide[dfWide$group == "cont", "highConf_abs"], dfWide[dfWide$group == "cont", paste0("FC_diff_s4")])
}
cor_cont
## rho lwr.ci upr.ci
## -0.1753589 -0.5335222 0.2361337
# experimental
if (cor == "spearman") {
cor_exp <- DescTools::SpearmanRho(dfWide[dfWide$group == "exp", "highConf_abs"], dfWide[dfWide$group == "exp", paste0("FC_diff_s4")], conf.level = 0.95)
} else {
cor_exp <- cor.test(dfWide[dfWide$group == "exp", "highConf_abs"], dfWide[dfWide$group == "exp", paste0("FC_diff_s4")])
}
cor_exp
## rho lwr.ci upr.ci
## 0.42606418 0.03719513 0.70286669
# group effect
if (cor == "spearman") {
cocor::cocor.indep.groups(cor_exp[1], cor_cont[1], 25, 25, alternative = "two.sided",
test = "all", alpha = 0.05, conf.level = 0.95, null.value = 0,
data.name = NULL, var.labels = NULL, return.htest = FALSE)
} else {
cocor::cocor.indep.groups(cor_exp$estimate, cor_cont$estimate, 25, 25, alternative = "two.sided",
test = "all", alpha = 0.05, conf.level = 0.95, null.value = 0,
data.name = NULL, var.labels = NULL, return.htest = FALSE)
}
##
## Results of a comparison of two correlations based on independent groups
##
## Comparison between r1.jk = 0.4261 and r2.hm = -0.1754
## Difference: r1.jk - r2.hm = 0.6014
## Group sizes: n1 = 25, n2 = 25
## Null hypothesis: r1.jk is equal to r2.hm
## Alternative hypothesis: r1.jk is not equal to r2.hm (two-sided)
## Alpha: 0.05
##
## fisher1925: Fisher's z (1925)
## z = 2.0970, p-value = 0.0360
## Null hypothesis rejected
##
## zou2007: Zou's (2007) confidence interval
## 95% confidence interval for r1.jk - r2.hm: 0.0353 1.0541
## Null hypothesis rejected (Interval does not include 0)
cor <- "spearman"
# whole sample
if (cor == "spearman") {
DescTools::SpearmanRho(dfWide[, "CDMB"], dfWide[, "FC_diff_s4"], conf.level = 0.95)
} else {
cor.test(dfWide[, "CDMB"], dfWide[, "FC_diff_s4"])
}
## rho lwr.ci upr.ci
## -0.02458642 -0.30087508 0.25550990
# control
if (cor == "spearman") {
cor_cont <- DescTools::SpearmanRho(dfWide[dfWide$group == "cont", "CDMB"], dfWide[dfWide$group == "cont", paste0("FC_diff_s4")], conf.level = 0.95)
} else {
cor_cont <- cor.test(dfWide[dfWide$group == "cont", "CDMB"], dfWide[dfWide$group == "cont", paste0("FC_diff_s4")])
}
cor_cont
## rho lwr.ci upr.ci
## -0.1654164 -0.5261571 0.2457789
# experimental
if (cor == "spearman") {
cor_exp <- DescTools::SpearmanRho(dfWide[dfWide$group == "exp", "CDMB"], dfWide[dfWide$group == "exp", paste0("FC_diff_s4")], conf.level = 0.95)
} else {
cor_exp <- cor.test(dfWide[dfWide$group == "exp", "CDMB"], dfWide[dfWide$group == "exp", paste0("FC_diff_s4")])
}
cor_exp
## rho lwr.ci upr.ci
## 0.006923077 -0.389272686 0.400957149
# group effect
if (cor == "spearman") {
cocor::cocor.indep.groups(cor_exp[1], cor_cont[1], 25, 25, alternative = "two.sided",
test = "all", alpha = 0.05, conf.level = 0.95, null.value = 0,
data.name = NULL, var.labels = NULL, return.htest = FALSE)
} else {
cocor::cocor.indep.groups(cor_exp$estimate, cor_cont$estimate, 25, 25, alternative = "two.sided",
test = "all", alpha = 0.05, conf.level = 0.95, null.value = 0,
data.name = NULL, var.labels = NULL, return.htest = FALSE)
}
##
## Results of a comparison of two correlations based on independent groups
##
## Comparison between r1.jk = 0.0069 and r2.hm = -0.1654
## Difference: r1.jk - r2.hm = 0.1723
## Group sizes: n1 = 25, n2 = 25
## Null hypothesis: r1.jk is equal to r2.hm
## Alternative hypothesis: r1.jk is not equal to r2.hm (two-sided)
## Alpha: 0.05
##
## fisher1925: Fisher's z (1925)
## z = 0.5767, p-value = 0.5642
## Null hypothesis retained
##
## zou2007: Zou's (2007) confidence interval
## 95% confidence interval for r1.jk - r2.hm: -0.3987 0.7066
## Null hypothesis retained (Interval includes 0)
FC connectivity during consolidation (i.e. changes in RSFC between HPC & midbrain) and encoding (task FC between HPC & midbrain) were correlated with the behavioural measurements of learning. This was done separately for each smoothing kernel applied during pre-processing.
Below, a graph was created for each behavioural measurement of learning plotting the different memory levels on the y-axis and correlation coefficients and their 95% confidence interval on the x-axis (dashed line is equivalent to r = 0). Correlation values were plotted for the whole sample and seperately for control- and incentives group. Additionally, their difference was plotted. Different colours and shape indicate different smoothing kernels (see legend). Data from the consolidation was plotted on the left side, whereas data on the right stems from encoding. The data used in the plot can be found at the end of the paper in form from tables.
Each table below shows the correlation between behavioural measures of learning and firstly, the change in RSFC in the context of consolidation and secondly, task-FC in the context of encoding.
note that all p < 0.05 were printed in bold whereas all 0.05 < p < 0.10 were printed in italics
| smooth | cor | pval | cor_cont | pval_cor_cont | cor_exp | pval_cor_exp | corrDiff | pval_corrDiff |
|---|---|---|---|---|---|---|---|---|
| Total # items encoded | ||||||||
| FWHM = 0 | 0.069 | 0.633 | -0.335 | 0.102 | 0.388 | 0.055 | 0.723 | 0.012 |
| FWHM = 4 | 0.080 | 0.580 | -0.313 | 0.128 | 0.389 | 0.054 | 0.702 | 0.015 |
| FWHM = 6 | 0.134 | 0.353 | -0.342 | 0.094 | 0.492 | 0.013 | 0.834 | 0.003 |
| FWHM = 8 | 0.165 | 0.253 | -0.328 | 0.110 | 0.536 | 0.006 | 0.864 | 0.002 |
| Curiosity-driven memory benefit | ||||||||
| FWHM = 0 | 0.058 | 0.690 | -0.166 | 0.427 | 0.220 | 0.290 | 0.386 | 0.194 |
| FWHM = 4 | 0.074 | 0.611 | -0.180 | 0.389 | 0.269 | 0.193 | 0.449 | 0.129 |
| FWHM = 6 | 0.113 | 0.435 | -0.108 | 0.606 | 0.273 | 0.186 | 0.382 | 0.197 |
| FWHM = 8 | 0.122 | 0.399 | -0.092 | 0.662 | 0.279 | 0.177 | 0.371 | 0.209 |
| Corrected memory | ||||||||
| FWHM = 0 | 0.056 | 0.700 | -0.277 | 0.180 | 0.316 | 0.124 | 0.594 | 0.042 |
| FWHM = 4 | 0.065 | 0.652 | -0.260 | 0.209 | 0.320 | 0.119 | 0.580 | 0.047 |
| FWHM = 6 | 0.116 | 0.423 | -0.302 | 0.142 | 0.428 | 0.033 | 0.730 | 0.011 |
| FWHM = 8 | 0.143 | 0.321 | -0.298 | 0.148 | 0.475 | 0.016 | 0.773 | 0.006 |
| Within-person correlation | ||||||||
| FWHM = 0 | -0.092 | 0.524 | -0.268 | 0.195 | 0.102 | 0.628 | 0.370 | 0.211 |
| FWHM = 4 | -0.093 | 0.523 | -0.287 | 0.165 | 0.126 | 0.548 | 0.413 | 0.162 |
| FWHM = 6 | -0.061 | 0.673 | -0.225 | 0.280 | 0.110 | 0.602 | 0.335 | 0.261 |
| FWHM = 8 | -0.049 | 0.738 | -0.203 | 0.330 | 0.113 | 0.591 | 0.316 | 0.289 |
| smooth | cor | pval | cor_cont | pval_cor_cont | cor_exp | pval_cor_exp | corrDiff | pval_corrDiff |
|---|---|---|---|---|---|---|---|---|
| Total # items encoded | ||||||||
| FWHM = 0 | -0.010 | 0.943 | 0.012 | 0.955 | -0.053 | 0.801 | -0.065 | 0.829 |
| FWHM = 4 | 0.029 | 0.843 | 0.068 | 0.745 | -0.028 | 0.895 | -0.096 | 0.749 |
| FWHM = 6 | 0.108 | 0.456 | 0.064 | 0.761 | 0.156 | 0.457 | 0.092 | 0.758 |
| FWHM = 8 | 0.099 | 0.494 | 0.031 | 0.882 | 0.180 | 0.390 | 0.148 | 0.618 |
| Curiosity-driven memory benefit | ||||||||
| FWHM = 0 | -0.117 | 0.417 | -0.222 | 0.287 | -0.092 | 0.660 | 0.129 | 0.660 |
| FWHM = 4 | -0.153 | 0.288 | -0.288 | 0.162 | -0.095 | 0.652 | 0.194 | 0.503 |
| FWHM = 6 | -0.131 | 0.364 | -0.314 | 0.126 | 0.030 | 0.886 | 0.344 | 0.238 |
| FWHM = 8 | -0.094 | 0.517 | -0.301 | 0.144 | 0.121 | 0.564 | 0.422 | 0.151 |
| Corrected memory | ||||||||
| FWHM = 0 | -0.020 | 0.889 | -0.004 | 0.987 | -0.059 | 0.779 | -0.056 | 0.853 |
| FWHM = 4 | 0.023 | 0.875 | 0.060 | 0.776 | -0.033 | 0.875 | -0.093 | 0.757 |
| FWHM = 6 | 0.105 | 0.469 | 0.063 | 0.765 | 0.149 | 0.476 | 0.086 | 0.772 |
| FWHM = 8 | 0.097 | 0.504 | 0.034 | 0.874 | 0.171 | 0.414 | 0.137 | 0.645 |
| Within-person correlation | ||||||||
| FWHM = 0 | -0.165 | 0.253 | -0.164 | 0.433 | -0.213 | 0.307 | -0.049 | 0.867 |
| FWHM = 4 | -0.197 | 0.171 | -0.225 | 0.279 | -0.198 | 0.342 | 0.027 | 0.924 |
| FWHM = 6 | -0.194 | 0.177 | -0.284 | 0.169 | -0.073 | 0.729 | 0.211 | 0.468 |
| FWHM = 8 | -0.162 | 0.260 | -0.292 | 0.156 | 0.044 | 0.833 | 0.337 | 0.252 |
| smooth | cor | pval | cor_cont | pval_cor_cont | cor_exp | pval_cor_exp | corrDiff | pval_corrDiff |
|---|---|---|---|---|---|---|---|---|
| Total # items encoded | ||||||||
| FWHM = 0 | 0.041 | 0.778 | -0.091 | 0.666 | 0.170 | 0.417 | 0.261 | 0.383 |
| FWHM = 4 | 0.064 | 0.657 | -0.047 | 0.822 | 0.166 | 0.428 | 0.213 | 0.476 |
| FWHM = 6 | 0.106 | 0.463 | 0.005 | 0.980 | 0.196 | 0.349 | 0.190 | 0.522 |
| FWHM = 8 | 0.102 | 0.483 | 0.009 | 0.965 | 0.185 | 0.376 | 0.176 | 0.555 |
| Curiosity-driven memory benefit | ||||||||
| FWHM = 0 | 0.009 | 0.950 | -0.283 | 0.170 | 0.359 | 0.078 | 0.642 | 0.027 |
| FWHM = 4 | -0.015 | 0.916 | -0.311 | 0.130 | 0.310 | 0.131 | 0.621 | 0.033 |
| FWHM = 6 | -0.086 | 0.553 | -0.323 | 0.115 | 0.156 | 0.457 | 0.479 | 0.103 |
| FWHM = 8 | -0.112 | 0.437 | -0.312 | 0.129 | 0.089 | 0.672 | 0.401 | 0.171 |
| Corrected memory | ||||||||
| FWHM = 0 | 0.062 | 0.671 | -0.040 | 0.850 | 0.163 | 0.436 | 0.203 | 0.498 |
| FWHM = 4 | 0.082 | 0.570 | 0.006 | 0.978 | 0.154 | 0.462 | 0.148 | 0.620 |
| FWHM = 6 | 0.116 | 0.423 | 0.048 | 0.818 | 0.177 | 0.397 | 0.129 | 0.665 |
| FWHM = 8 | 0.109 | 0.451 | 0.044 | 0.836 | 0.170 | 0.417 | 0.126 | 0.672 |
| Within-person correlation | ||||||||
| FWHM = 0 | -0.086 | 0.552 | -0.298 | 0.148 | 0.237 | 0.254 | 0.535 | 0.069 |
| FWHM = 4 | -0.093 | 0.520 | -0.313 | 0.128 | 0.216 | 0.300 | 0.529 | 0.072 |
| FWHM = 6 | -0.126 | 0.385 | -0.315 | 0.126 | 0.128 | 0.540 | 0.443 | 0.131 |
| FWHM = 8 | -0.150 | 0.298 | -0.316 | 0.124 | 0.075 | 0.722 | 0.390 | 0.183 |
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 15.80 | 13.68 – 17.92 | 15.04 | <0.001 | 15.52 | 13.57 – 17.47 | 16.58 | <0.001 | 14.76 | 12.37 – 17.15 | 12.83 | <0.001 |
| Online FC | -38.02 | -127.09 – 51.05 | -0.86 | 0.394 | 0.45 | -108.70 – 109.60 | 0.01 | 0.993 | -91.19 | -254.82 – 72.44 | -1.16 | 0.260 |
| Offline FC | 20.68 | -44.56 – 85.91 | 0.64 | 0.526 | -0.13 | -83.98 – 83.72 | -0.00 | 0.997 | 36.99 | -73.31 – 147.28 | 0.70 | 0.493 |
| Incentives | -0.85 | -3.85 – 2.16 | -0.57 | 0.574 | ||||||||
| RSFC change | -59.01 | -133.41 – 15.40 | -1.60 | 0.117 | -52.49 | -123.81 – 18.84 | -1.53 | 0.141 | 95.19 | 0.73 – 189.64 | 2.10 | 0.048 |
| Incentives * RSFC change | 146.80 | 39.20 – 254.39 | 2.75 | 0.009 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.156 / 0.060 | 0.112 / -0.015 | 0.207 / 0.094 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 15.80 | 13.68 – 17.92 | 15.04 | <0.001 |
| Online FC | -38.02 | -127.09 – 51.05 | -0.86 | 0.394 |
| Offline FC | 20.68 | -44.56 – 85.91 | 0.64 | 0.526 |
| Incentives | -0.85 | -3.85 – 2.16 | -0.57 | 0.574 |
| RSFC change | -59.01 | -133.41 – 15.40 | -1.60 | 0.117 |
| Incentives * RSFC change | 146.80 | 39.20 – 254.39 | 2.75 | 0.009 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.156 / 0.060 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 0.10 | 0.01 – 0.18 | 2.22 | 0.032 | 0.08 | -0.00 – 0.17 | 1.98 | 0.061 | -0.05 | -0.13 – 0.04 | -1.10 | 0.283 |
| Online FC | -2.89 | -6.52 – 0.74 | -1.61 | 0.115 | -1.24 | -6.15 – 3.67 | -0.53 | 0.604 | -3.91 | -9.74 – 1.92 | -1.40 | 0.177 |
| Offline FC | 1.02 | -1.64 – 3.68 | 0.78 | 0.442 | -1.29 | -5.06 – 2.49 | -0.71 | 0.486 | 3.67 | -0.26 – 7.60 | 1.94 | 0.066 |
| Incentives | -0.14 | -0.26 – -0.02 | -2.31 | 0.026 | ||||||||
| RSFC change | -1.51 | -4.54 – 1.52 | -1.00 | 0.322 | -0.84 | -4.05 – 2.36 | -0.55 | 0.590 | 1.76 | -1.60 – 5.13 | 1.09 | 0.288 |
| Incentives * RSFC change | 3.60 | -0.78 – 7.99 | 1.66 | 0.105 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.171 / 0.077 | 0.101 / -0.028 | 0.220 / 0.109 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 0.10 | 0.01 – 0.18 | 2.22 | 0.032 |
| Online FC | -2.89 | -6.52 – 0.74 | -1.61 | 0.115 |
| Offline FC | 1.02 | -1.64 – 3.68 | 0.78 | 0.442 |
| Incentives | -0.14 | -0.26 – -0.02 | -2.31 | 0.026 |
| RSFC change | -1.51 | -4.54 – 1.52 | -1.00 | 0.322 |
| Incentives * RSFC change | 3.60 | -0.78 – 7.99 | 1.66 | 0.105 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.171 / 0.077 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 14.53 | 12.33 – 16.72 | 13.35 | <0.001 | 14.26 | 12.25 – 16.27 | 14.74 | <0.001 | 13.45 | 10.95 – 15.95 | 11.18 | <0.001 |
| Online FC | -41.29 | -133.55 – 50.97 | -0.90 | 0.372 | -10.07 | -122.86 – 102.72 | -0.19 | 0.854 | -85.06 | -256.13 – 86.00 | -1.03 | 0.313 |
| Offline FC | 26.63 | -40.95 – 94.20 | 0.79 | 0.431 | 10.43 | -76.22 – 97.07 | 0.25 | 0.805 | 38.92 | -76.39 – 154.22 | 0.70 | 0.490 |
| Incentives | -0.94 | -4.05 – 2.17 | -0.61 | 0.546 | ||||||||
| RSFC change | -52.33 | -129.40 – 24.75 | -1.37 | 0.178 | -47.22 | -120.93 – 26.48 | -1.33 | 0.197 | 78.72 | -20.03 – 177.47 | 1.66 | 0.112 |
| Incentives * RSFC change | 124.73 | 13.28 – 236.18 | 2.26 | 0.029 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.117 / 0.016 | 0.080 / -0.052 | 0.151 / 0.030 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 14.53 | 12.33 – 16.72 | 13.35 | <0.001 |
| Online FC | -41.29 | -133.55 – 50.97 | -0.90 | 0.372 |
| Offline FC | 26.63 | -40.95 – 94.20 | 0.79 | 0.431 |
| Incentives | -0.94 | -4.05 – 2.17 | -0.61 | 0.546 |
| RSFC change | -52.33 | -129.40 – 24.75 | -1.37 | 0.178 |
| Incentives * RSFC change | 124.73 | 13.28 – 236.18 | 2.26 | 0.029 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.117 / 0.016 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 0.07 | -0.01 – 0.14 | 1.79 | 0.080 | 0.06 | -0.03 – 0.14 | 1.36 | 0.187 | 0.01 | -0.06 – 0.08 | 0.35 | 0.733 |
| Online FC | -2.25 | -5.48 – 0.98 | -1.41 | 0.167 | -0.60 | -5.42 – 4.22 | -0.26 | 0.798 | -3.74 | -8.41 – 0.93 | -1.67 | 0.111 |
| Offline FC | 0.33 | -2.03 – 2.70 | 0.28 | 0.777 | -1.45 | -5.16 – 2.25 | -0.82 | 0.423 | 2.26 | -0.88 – 5.41 | 1.50 | 0.150 |
| Incentives | -0.06 | -0.17 – 0.05 | -1.09 | 0.282 | ||||||||
| RSFC change | -2.02 | -4.71 – 0.68 | -1.51 | 0.139 | -1.49 | -4.64 – 1.66 | -0.99 | 0.335 | 0.91 | -1.78 – 3.61 | 0.70 | 0.489 |
| Incentives * RSFC change | 3.02 | -0.88 – 6.92 | 1.56 | 0.126 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.104 / 0.003 | 0.129 / 0.005 | 0.168 / 0.049 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 0.07 | -0.01 – 0.14 | 1.79 | 0.080 |
| Online FC | -2.25 | -5.48 – 0.98 | -1.41 | 0.167 |
| Offline FC | 0.33 | -2.03 – 2.70 | 0.28 | 0.777 |
| Incentives | -0.06 | -0.17 – 0.05 | -1.09 | 0.282 |
| RSFC change | -2.02 | -4.71 – 0.68 | -1.51 | 0.139 |
| Incentives * RSFC change | 3.02 | -0.88 – 6.92 | 1.56 | 0.126 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.104 / 0.003 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 15.75 | 13.61 – 17.88 | 14.88 | <0.001 | 15.52 | 13.56 – 17.48 | 16.50 | <0.001 | 14.76 | 12.35 – 17.17 | 12.72 | <0.001 |
| Online FC | -20.10 | -103.75 – 63.55 | -0.48 | 0.631 | 15.36 | -85.45 – 116.16 | 0.32 | 0.755 | -74.39 | -229.93 – 81.14 | -0.99 | 0.331 |
| Offline FC | 16.59 | -43.56 – 76.75 | 0.56 | 0.581 | 0.15 | -78.35 – 78.65 | 0.00 | 0.997 | 30.12 | -68.41 – 128.64 | 0.64 | 0.532 |
| Incentives | -0.77 | -3.80 – 2.26 | -0.51 | 0.611 | ||||||||
| RSFC change | -49.44 | -118.33 – 19.46 | -1.45 | 0.155 | -45.68 | -111.28 – 19.92 | -1.45 | 0.162 | 84.79 | -2.77 – 172.35 | 2.01 | 0.057 |
| Incentives * RSFC change | 127.24 | 28.24 – 226.23 | 2.59 | 0.013 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.143 / 0.046 | 0.104 / -0.024 | 0.194 / 0.078 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 15.75 | 13.61 – 17.88 | 14.88 | <0.001 |
| Online FC | -20.10 | -103.75 – 63.55 | -0.48 | 0.631 |
| Offline FC | 16.59 | -43.56 – 76.75 | 0.56 | 0.581 |
| Incentives | -0.77 | -3.80 – 2.26 | -0.51 | 0.611 |
| RSFC change | -49.44 | -118.33 – 19.46 | -1.45 | 0.155 |
| Incentives * RSFC change | 127.24 | 28.24 – 226.23 | 2.59 | 0.013 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.143 / 0.046 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 0.10 | 0.01 – 0.18 | 2.27 | 0.028 | 0.08 | -0.00 – 0.17 | 2.02 | 0.057 | -0.05 | -0.13 – 0.04 | -1.10 | 0.284 |
| Online FC | -3.01 | -6.34 – 0.32 | -1.82 | 0.075 | -1.70 | -6.13 – 2.73 | -0.80 | 0.435 | -4.00 | -9.50 – 1.50 | -1.51 | 0.145 |
| Offline FC | 0.82 | -1.58 – 3.21 | 0.69 | 0.494 | -1.15 | -4.60 – 2.30 | -0.69 | 0.496 | 2.86 | -0.62 – 6.35 | 1.71 | 0.102 |
| Incentives | -0.14 | -0.26 – -0.02 | -2.35 | 0.023 | ||||||||
| RSFC change | -1.32 | -4.07 – 1.42 | -0.97 | 0.336 | -0.81 | -3.69 – 2.08 | -0.58 | 0.567 | 1.99 | -1.11 – 5.08 | 1.34 | 0.196 |
| Incentives * RSFC change | 3.63 | -0.31 – 7.58 | 1.86 | 0.070 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.197 / 0.106 | 0.132 / 0.008 | 0.218 / 0.107 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 0.10 | 0.01 – 0.18 | 2.27 | 0.028 |
| Online FC | -3.01 | -6.34 – 0.32 | -1.82 | 0.075 |
| Offline FC | 0.82 | -1.58 – 3.21 | 0.69 | 0.494 |
| Incentives | -0.14 | -0.26 – -0.02 | -2.35 | 0.023 |
| RSFC change | -1.32 | -4.07 – 1.42 | -0.97 | 0.336 |
| Incentives * RSFC change | 3.63 | -0.31 – 7.58 | 1.86 | 0.070 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.197 / 0.106 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 14.47 | 12.27 – 16.68 | 13.22 | <0.001 | 14.26 | 12.24 – 16.28 | 14.71 | <0.001 | 13.45 | 10.93 – 15.97 | 11.10 | <0.001 |
| Online FC | -22.54 | -109.06 – 63.99 | -0.52 | 0.602 | 6.62 | -97.32 – 110.55 | 0.13 | 0.896 | -68.65 | -231.03 – 93.72 | -0.88 | 0.389 |
| Offline FC | 21.76 | -40.46 – 83.98 | 0.70 | 0.485 | 10.18 | -70.75 – 91.11 | 0.26 | 0.796 | 30.67 | -72.18 – 133.53 | 0.62 | 0.542 |
| Incentives | -0.86 | -4.00 – 2.27 | -0.56 | 0.581 | ||||||||
| RSFC change | -43.83 | -115.09 – 27.43 | -1.24 | 0.222 | -41.28 | -108.91 – 26.36 | -1.27 | 0.218 | 70.28 | -21.13 – 161.70 | 1.60 | 0.125 |
| Incentives * RSFC change | 107.53 | 5.14 – 209.93 | 2.12 | 0.040 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.106 / 0.004 | 0.075 / -0.057 | 0.139 / 0.016 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 14.47 | 12.27 – 16.68 | 13.22 | <0.001 |
| Online FC | -22.54 | -109.06 – 63.99 | -0.52 | 0.602 |
| Offline FC | 21.76 | -40.46 – 83.98 | 0.70 | 0.485 |
| Incentives | -0.86 | -4.00 – 2.27 | -0.56 | 0.581 |
| RSFC change | -43.83 | -115.09 – 27.43 | -1.24 | 0.222 |
| Incentives * RSFC change | 107.53 | 5.14 – 209.93 | 2.12 | 0.040 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.106 / 0.004 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 0.07 | -0.01 – 0.14 | 1.83 | 0.074 | 0.06 | -0.03 – 0.14 | 1.38 | 0.182 | 0.01 | -0.06 – 0.08 | 0.35 | 0.733 |
| Online FC | -2.35 | -5.32 – 0.63 | -1.59 | 0.119 | -1.07 | -5.44 – 3.31 | -0.51 | 0.617 | -3.57 | -7.98 – 0.85 | -1.68 | 0.108 |
| Offline FC | 0.36 | -1.78 – 2.50 | 0.34 | 0.735 | -1.21 | -4.61 – 2.20 | -0.74 | 0.469 | 1.96 | -0.84 – 4.75 | 1.46 | 0.160 |
| Incentives | -0.06 | -0.17 – 0.05 | -1.12 | 0.268 | ||||||||
| RSFC change | -1.90 | -4.35 – 0.55 | -1.56 | 0.126 | -1.49 | -4.34 – 1.36 | -1.09 | 0.288 | 0.90 | -1.58 – 3.38 | 0.75 | 0.460 |
| Incentives * RSFC change | 2.96 | -0.56 – 6.48 | 1.69 | 0.097 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.124 / 0.024 | 0.151 / 0.030 | 0.164 / 0.045 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 0.07 | -0.01 – 0.14 | 1.83 | 0.074 |
| Online FC | -2.35 | -5.32 – 0.63 | -1.59 | 0.119 |
| Offline FC | 0.36 | -1.78 – 2.50 | 0.34 | 0.735 |
| Incentives | -0.06 | -0.17 – 0.05 | -1.12 | 0.268 |
| RSFC change | -1.90 | -4.35 – 0.55 | -1.56 | 0.126 |
| Incentives * RSFC change | 2.96 | -0.56 – 6.48 | 1.69 | 0.097 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.124 / 0.024 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 15.70 | 13.66 – 17.75 | 15.47 | <0.001 | 15.52 | 13.60 – 17.44 | 16.78 | <0.001 | 14.76 | 12.45 – 17.07 | 13.30 | <0.001 |
| Online FC | -1.80 | -53.75 – 50.15 | -0.07 | 0.945 | 13.19 | -47.78 – 74.16 | 0.45 | 0.657 | -32.15 | -138.54 – 74.24 | -0.63 | 0.536 |
| Offline FC | 10.12 | -27.38 – 47.62 | 0.54 | 0.589 | 0.79 | -49.54 – 51.12 | 0.03 | 0.974 | 21.49 | -39.35 – 82.33 | 0.73 | 0.471 |
| Incentives | -0.65 | -3.55 – 2.25 | -0.45 | 0.652 | ||||||||
| RSFC change | -35.06 | -78.33 – 8.21 | -1.63 | 0.110 | -35.03 | -76.60 – 6.54 | -1.75 | 0.094 | 64.17 | 11.15 – 117.20 | 2.52 | 0.020 |
| Incentives * RSFC change | 94.35 | 33.95 – 154.74 | 3.15 | 0.003 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.207 / 0.117 | 0.133 / 0.010 | 0.263 / 0.158 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 15.70 | 13.66 – 17.75 | 15.47 | <0.001 |
| Online FC | -1.80 | -53.75 – 50.15 | -0.07 | 0.945 |
| Offline FC | 10.12 | -27.38 – 47.62 | 0.54 | 0.589 |
| Incentives | -0.65 | -3.55 – 2.25 | -0.45 | 0.652 |
| RSFC change | -35.06 | -78.33 – 8.21 | -1.63 | 0.110 |
| Incentives * RSFC change | 94.35 | 33.95 – 154.74 | 3.15 | 0.003 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.207 / 0.117 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 0.09 | 0.00 – 0.18 | 2.10 | 0.042 | 0.08 | -0.00 – 0.17 | 2.00 | 0.058 | -0.05 | -0.14 – 0.05 | -1.03 | 0.313 |
| Online FC | -1.36 | -3.54 – 0.83 | -1.25 | 0.218 | -0.83 | -3.58 – 1.91 | -0.63 | 0.533 | -1.72 | -5.90 – 2.47 | -0.85 | 0.403 |
| Offline FC | 0.14 | -1.44 – 1.72 | 0.18 | 0.861 | -0.77 | -3.03 – 1.50 | -0.70 | 0.490 | 1.00 | -1.39 – 3.40 | 0.87 | 0.393 |
| Incentives | -0.13 | -0.25 – -0.01 | -2.18 | 0.035 | ||||||||
| RSFC change | -0.28 | -2.10 – 1.54 | -0.31 | 0.758 | -0.13 | -2.00 – 1.74 | -0.14 | 0.889 | 1.41 | -0.67 – 3.50 | 1.41 | 0.173 |
| Incentives * RSFC change | 1.80 | -0.74 – 4.35 | 1.43 | 0.160 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.168 / 0.074 | 0.122 / -0.003 | 0.115 / -0.012 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 0.09 | 0.00 – 0.18 | 2.10 | 0.042 |
| Online FC | -1.36 | -3.54 – 0.83 | -1.25 | 0.218 |
| Offline FC | 0.14 | -1.44 – 1.72 | 0.18 | 0.861 |
| Incentives | -0.13 | -0.25 – -0.01 | -2.18 | 0.035 |
| RSFC change | -0.28 | -2.10 – 1.54 | -0.31 | 0.758 |
| Incentives * RSFC change | 1.80 | -0.74 – 4.35 | 1.43 | 0.160 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.168 / 0.074 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 14.43 | 12.30 – 16.56 | 13.68 | <0.001 | 14.26 | 12.28 – 16.24 | 14.98 | <0.001 | 13.45 | 11.02 – 15.88 | 11.50 | <0.001 |
| Online FC | -2.75 | -56.75 – 51.26 | -0.10 | 0.919 | 7.27 | -55.48 – 70.01 | 0.24 | 0.812 | -24.68 | -136.79 – 87.44 | -0.46 | 0.652 |
| Offline FC | 12.14 | -26.84 – 51.12 | 0.63 | 0.533 | 7.28 | -44.51 – 59.08 | 0.29 | 0.773 | 18.62 | -45.49 – 82.74 | 0.60 | 0.552 |
| Incentives | -0.73 | -3.75 – 2.28 | -0.49 | 0.627 | ||||||||
| RSFC change | -32.05 | -77.03 – 12.93 | -1.44 | 0.158 | -32.39 | -75.18 – 10.39 | -1.57 | 0.130 | 55.65 | -0.23 – 111.53 | 2.07 | 0.051 |
| Incentives * RSFC change | 83.78 | 21.00 – 146.56 | 2.69 | 0.010 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.164 / 0.069 | 0.109 / -0.018 | 0.198 / 0.083 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 14.43 | 12.30 – 16.56 | 13.68 | <0.001 |
| Online FC | -2.75 | -56.75 – 51.26 | -0.10 | 0.919 |
| Offline FC | 12.14 | -26.84 – 51.12 | 0.63 | 0.533 |
| Incentives | -0.73 | -3.75 – 2.28 | -0.49 | 0.627 |
| RSFC change | -32.05 | -77.03 – 12.93 | -1.44 | 0.158 |
| Incentives * RSFC change | 83.78 | 21.00 – 146.56 | 2.69 | 0.010 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.164 / 0.069 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 0.06 | -0.01 – 0.14 | 1.69 | 0.099 | 0.06 | -0.03 – 0.14 | 1.36 | 0.187 | 0.01 | -0.06 – 0.08 | 0.33 | 0.746 |
| Online FC | -1.18 | -3.14 – 0.78 | -1.21 | 0.231 | -0.60 | -3.32 – 2.12 | -0.46 | 0.653 | -1.76 | -5.08 – 1.56 | -1.10 | 0.282 |
| Offline FC | 0.11 | -1.30 – 1.53 | 0.16 | 0.871 | -0.74 | -2.99 – 1.50 | -0.69 | 0.499 | 0.96 | -0.94 – 2.85 | 1.05 | 0.306 |
| Incentives | -0.05 | -0.16 – 0.06 | -1.00 | 0.324 | ||||||||
| RSFC change | -0.80 | -2.43 – 0.83 | -0.99 | 0.329 | -0.67 | -2.52 – 1.19 | -0.75 | 0.464 | 0.59 | -1.06 – 2.25 | 0.75 | 0.463 |
| Incentives * RSFC change | 1.44 | -0.83 – 3.72 | 1.28 | 0.208 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.091 / -0.012 | 0.131 / 0.007 | 0.077 / -0.055 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 0.06 | -0.01 – 0.14 | 1.69 | 0.099 |
| Online FC | -1.18 | -3.14 – 0.78 | -1.21 | 0.231 |
| Offline FC | 0.11 | -1.30 – 1.53 | 0.16 | 0.871 |
| Incentives | -0.05 | -0.16 – 0.06 | -1.00 | 0.324 |
| RSFC change | -0.80 | -2.43 – 0.83 | -0.99 | 0.329 |
| Incentives * RSFC change | 1.44 | -0.83 – 3.72 | 1.28 | 0.208 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.091 / -0.012 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 15.68 | 13.67 – 17.70 | 15.71 | <0.001 | 15.52 | 13.59 – 17.45 | 16.70 | <0.001 | 14.76 | 12.52 – 17.00 | 13.71 | <0.001 |
| Online FC | -1.07 | -41.32 – 39.18 | -0.05 | 0.958 | 10.24 | -38.01 – 58.50 | 0.44 | 0.663 | -26.23 | -109.02 – 56.56 | -0.66 | 0.517 |
| Offline FC | 6.85 | -22.61 – 36.31 | 0.47 | 0.642 | 0.19 | -40.34 – 40.72 | 0.01 | 0.992 | 15.86 | -31.37 – 63.09 | 0.70 | 0.493 |
| Incentives | -0.62 | -3.47 – 2.24 | -0.44 | 0.665 | ||||||||
| RSFC change | -25.68 | -58.79 – 7.43 | -1.56 | 0.125 | -26.92 | -59.42 – 5.58 | -1.72 | 0.100 | 54.25 | 14.49 – 94.02 | 2.84 | 0.010 |
| Incentives * RSFC change | 75.42 | 30.06 – 120.78 | 3.35 | 0.002 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.231 / 0.143 | 0.125 / 0.000 | 0.306 / 0.207 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 15.68 | 13.67 – 17.70 | 15.71 | <0.001 |
| Online FC | -1.07 | -41.32 – 39.18 | -0.05 | 0.958 |
| Offline FC | 6.85 | -22.61 – 36.31 | 0.47 | 0.642 |
| Incentives | -0.62 | -3.47 – 2.24 | -0.44 | 0.665 |
| RSFC change | -25.68 | -58.79 – 7.43 | -1.56 | 0.125 |
| Incentives * RSFC change | 75.42 | 30.06 – 120.78 | 3.35 | 0.002 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.231 / 0.143 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 0.09 | -0.00 – 0.17 | 2.01 | 0.050 | 0.08 | -0.00 – 0.17 | 1.99 | 0.060 | -0.05 | -0.14 – 0.05 | -1.01 | 0.322 |
| Online FC | -0.58 | -2.31 – 1.16 | -0.67 | 0.505 | -0.52 | -2.70 – 1.66 | -0.50 | 0.625 | -0.13 | -3.55 – 3.30 | -0.08 | 0.939 |
| Offline FC | -0.16 | -1.43 – 1.11 | -0.25 | 0.806 | -0.56 | -2.39 – 1.27 | -0.64 | 0.530 | 0.14 | -1.82 – 2.09 | 0.15 | 0.885 |
| Incentives | -0.13 | -0.25 – -0.00 | -2.09 | 0.043 | ||||||||
| RSFC change | -0.09 | -1.52 – 1.34 | -0.13 | 0.901 | 0.02 | -1.45 – 1.48 | 0.02 | 0.983 | 0.95 | -0.69 – 2.60 | 1.21 | 0.241 |
| Incentives * RSFC change | 1.23 | -0.72 – 3.19 | 1.27 | 0.211 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.155 / 0.058 | 0.108 / -0.019 | 0.079 / -0.053 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 0.09 | -0.00 – 0.17 | 2.01 | 0.050 |
| Online FC | -0.58 | -2.31 – 1.16 | -0.67 | 0.505 |
| Offline FC | -0.16 | -1.43 – 1.11 | -0.25 | 0.806 |
| Incentives | -0.13 | -0.25 – -0.00 | -2.09 | 0.043 |
| RSFC change | -0.09 | -1.52 – 1.34 | -0.13 | 0.901 |
| Incentives * RSFC change | 1.23 | -0.72 – 3.19 | 1.27 | 0.211 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.155 / 0.058 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 14.42 | 12.32 – 16.51 | 13.88 | <0.001 | 14.26 | 12.28 – 16.24 | 14.97 | <0.001 | 13.45 | 11.08 – 15.82 | 11.81 | <0.001 |
| Online FC | -1.87 | -43.74 – 40.00 | -0.09 | 0.929 | 5.93 | -43.54 – 55.41 | 0.25 | 0.805 | -20.88 | -108.47 – 66.71 | -0.50 | 0.625 |
| Offline FC | 8.29 | -22.36 – 38.94 | 0.55 | 0.588 | 4.76 | -36.80 – 46.31 | 0.24 | 0.814 | 13.80 | -36.17 – 63.77 | 0.57 | 0.572 |
| Incentives | -0.70 | -3.67 – 2.27 | -0.47 | 0.637 | ||||||||
| RSFC change | -24.05 | -58.49 – 10.39 | -1.41 | 0.166 | -25.22 | -58.54 – 8.11 | -1.57 | 0.130 | 48.01 | 5.94 – 90.08 | 2.37 | 0.027 |
| Incentives * RSFC change | 68.34 | 21.16 – 115.53 | 2.92 | 0.006 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.188 / 0.095 | 0.107 / -0.020 | 0.239 / 0.130 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 14.42 | 12.32 – 16.51 | 13.88 | <0.001 |
| Online FC | -1.87 | -43.74 – 40.00 | -0.09 | 0.929 |
| Offline FC | 8.29 | -22.36 – 38.94 | 0.55 | 0.588 |
| Incentives | -0.70 | -3.67 – 2.27 | -0.47 | 0.637 |
| RSFC change | -24.05 | -58.49 – 10.39 | -1.41 | 0.166 |
| Incentives * RSFC change | 68.34 | 21.16 – 115.53 | 2.92 | 0.006 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.188 / 0.095 | |||
| Whole sample | Control group | Incentives group | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p | Estimates | CI | Statistic | p |
| Intercept | 0.06 | -0.02 – 0.14 | 1.60 | 0.117 | 0.06 | -0.03 – 0.14 | 1.36 | 0.190 | 0.01 | -0.06 – 0.09 | 0.32 | 0.753 |
| Online FC | -0.50 | -2.06 – 1.05 | -0.65 | 0.519 | -0.34 | -2.50 – 1.82 | -0.33 | 0.746 | -0.29 | -3.03 – 2.46 | -0.22 | 0.830 |
| Offline FC | -0.13 | -1.27 – 1.01 | -0.23 | 0.818 | -0.59 | -2.40 – 1.22 | -0.68 | 0.505 | 0.24 | -1.33 – 1.81 | 0.32 | 0.753 |
| Incentives | -0.05 | -0.16 – 0.06 | -0.91 | 0.367 | ||||||||
| RSFC change | -0.48 | -1.76 – 0.80 | -0.76 | 0.454 | -0.39 | -1.84 – 1.06 | -0.56 | 0.584 | 0.32 | -1.00 – 1.64 | 0.50 | 0.620 |
| Incentives * RSFC change | 0.94 | -0.81 – 2.70 | 1.08 | 0.285 | ||||||||
| Observations | 50 | 25 | 25 | |||||||||
| R2 / R2 adjusted | 0.071 / -0.034 | 0.120 / -0.006 | 0.018 / -0.123 | |||||||||
| Whole sample | ||||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| Intercept | 0.06 | -0.02 – 0.14 | 1.60 | 0.117 |
| Online FC | -0.50 | -2.06 – 1.05 | -0.65 | 0.519 |
| Offline FC | -0.13 | -1.27 – 1.01 | -0.23 | 0.818 |
| Incentives | -0.05 | -0.16 – 0.06 | -0.91 | 0.367 |
| RSFC change | -0.48 | -1.76 – 0.80 | -0.76 | 0.454 |
| Incentives * RSFC change | 0.94 | -0.81 – 2.70 | 1.08 | 0.285 |
| Observations | 50 | |||
| R2 / R2 adjusted | 0.071 / -0.034 | |||